This lack of data for opinion summarization has motivated many abstractive summarization methods based on auto-encoders
[1]}, [2]}, [3]},
and these do not use any supervision from gold human summaries. A few recent
approaches propose self-training of encoder-decoder
models on synthetic summary examples. These examples are created by randomly sampling one of the
input reviews and treating it as a pseudo-summary and treating other topically-related reviews as the source texts.[4]}, [5]}, [6]}, [7]}, [8]}
While such pseudo or silver-summaries are able to provide pretraining signals, their
objective is one of missing review prediction rather than aggregation of multiple texts.
Their pseudo-summaries are also entire reviews which might contain other non-summary worthy content.
